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To date, there have been over 200 million known proteins, but only 0.2% of them have well-annotated functional terms. By measuring the contacts among residues, proteins can be described as graphs so that the graph leaning approaches can be applied to learn protein representations. However, existing graph-based methods put efforts in enriching the residue node information and did not fully exploit the edge information, which leads to suboptimal representations considering the strong association of residue contacts to protein structures and to the functions. In this article, we propose SuperEdgeGO, which introduces the supervision of edges in protein graphs to learn a better graph representation for protein function prediction. Different from common graph convolution methods that uses edge information in a plain or unsupervised way, we introduce a <jats:italic>supervised<\/jats:italic> attention to encode the residue contacts <jats:italic>explicitly<\/jats:italic> into the protein representation. Comprehensive experiments demonstrate that SuperEdgeGO achieves state-of-the-art performance on all three categories of protein functions. Additional ablation analysis further proves the effectiveness of the devised edge supervision strategy. The implementation of edge supervision in SuperEdgeGO resulted in enhanced graph representations for protein function prediction, as demonstrated by its superior performance across all the evaluated categories. This superior performance was confirmed through ablation analysis, which validated the effectiveness of the edge supervision strategy. This strategy has a broad application prospect in the study of protein function and related fields.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013343","type":"journal-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T18:01:06Z","timestamp":1754071266000},"page":"e1013343","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["SuperEdgeGO: Edge-supervised graph representation learning for enhanced protein function prediction"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9774-9709","authenticated-orcid":true,"given":"Shugang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuntong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjian","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangpeng","family":"Bi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huasen","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"issue":"2","key":"pcbi.1013343.ref001","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s12539-024-00626-x","article-title":"Review and comparative analysis of methods and advancements in predicting protein complex structure","volume":"16","author":"N Zhao","year":"2024","journal-title":"Interdiscip Sci."},{"key":"pcbi.1013343.ref002","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gky1100","article-title":"InterPro in 2019 : improving coverage, classification and access to protein sequence annotations","volume":"47","author":"AL Mitchell","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"pcbi.1013343.ref003","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkw1098","article-title":"CATH: an expanded resource to predict protein function through structure and sequence","volume":"45","author":"NL Dawson","year":"2017","journal-title":"Nucleic Acids Res."},{"issue":"6","key":"pcbi.1013343.ref004","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1093\/bioinformatics\/btu744","article-title":"DISOPRED3: precise disordered region predictions with annotated protein-binding activity","volume":"31","author":"DT Jones","year":"2015","journal-title":"Bioinformatics."},{"issue":"1","key":"pcbi.1013343.ref005","doi-asserted-by":"crossref","first-page":"3168","DOI":"10.1038\/s41467-021-23303-9","article-title":"Structure-based protein function prediction using graph convolutional networks","volume":"12","author":"V Gligorijevi\u0107","year":"2021","journal-title":"Nat Commun."},{"key":"pcbi.1013343.ref006","unstructured":"The UniProt Knowledgebase. 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